Diego Ulises Ferreiro

FrustraEvo calculates and quantifies the conservation of local energetic frustration in protein families to identify evolutionary stability and functional constraints.

The paper analyzes local energetic frustration in protein families to uncover stability and function constraints, demonstrating its application to SARS-CoV-2 and poorly characterized proteins.

FrustraPocket identifies protein-ligand binding sites from unbound protein structures by detecting regions of local energetic frustration.

The paper introduces mebipred, a machine learning tool for predicting metal-binding proteins from sequence data, achieving 90% accuracy and outperforming existing methods.

FrustratometeR computes local energetic frustration in protein structures, point mutants, and MD simulations for structural analysis.

The paper shows that disordered proteins in fuzzy complexes retain residual frustration, leading to interaction versatility with different partners.

The paper analyzes a large dataset of Ankyrin-containing proteins to characterize the structure, abundance, and energetics of their repetitive regions, finding that longer arrays have more similar repeats and favorable folding energy.